Data Organization Best Practices for Reproducibility

𝐘𝐨𝐮 𝐨𝐩𝐞𝐧 𝐚 𝐟𝐨𝐥𝐝𝐞𝐫 𝐟𝐫𝐨𝐦 𝐬𝐢𝐱 𝐦𝐨𝐧𝐭𝐡𝐬 𝐚𝐠𝐨 And you’re greeted by 𝙖𝙣𝙖𝙡𝙮𝙨𝙞𝙨_𝙛𝙞𝙣𝙖𝙡_𝙫2_𝙍𝙀𝘼𝙇.𝙘𝙨𝙫𝙖𝙣𝙙𝙥𝙡𝙤𝙩_𝙣𝙚𝙬_𝙛𝙞𝙭𝙚𝙙.𝙥𝙣𝙜 Which one was the actual final version? Which script generated it? Bad data organization is the "silent killer" of scientific reproducibility. There is a massive pressure to publish, and we collect more data than ever before, but without a standardized system, that data becomes a graveyard of lost insights. Here, therefore, some practical advice: 𝐓𝐡𝐞 𝐆𝐨𝐥𝐝𝐞𝐧 𝐑𝐮𝐥𝐞𝐬 1) 𝐍𝐞𝐯𝐞𝐫 𝐦𝐨𝐝𝐢𝐟𝐲 𝐫𝐚𝐰 𝐝𝐚𝐭𝐚 Treat raw data files as read-only. All transformations go to a separate processed/ folder. 2) 𝐔𝐬𝐞 𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐧𝐚𝐦𝐢𝐧𝐠 Pick a convention on day one and follow it for every file in the project. 3) 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 Future-you is a stranger. Write README files and data dictionaries. - 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮 𝐜𝐚𝐧 Scripts are better than memory. If you click 20 times, write a script instead. I’ve compiled these best practices into a complete guide, including a copy-paste folder templates tool and a checklist for your next project. Link to the full guide in the first comment below. #research #phd #science #data

𝐒𝐭𝐨𝐩 𝐑𝐮𝐧𝐧𝐢𝐧𝐠 𝐒𝐜𝐫𝐢𝐩𝐭𝐬. 𝐒𝐭𝐚𝐫𝐭 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐒𝐜𝐢𝐞𝐧𝐜𝐞. 📊💻 Most people can type a line of code to run a regression. Very few can statistically defend the results. Join us for 𝐒𝐞𝐬𝐬𝐢𝐨𝐧 4 𝐨𝐟 "𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐈𝐧 𝐀𝐜𝐭𝐢𝐨𝐧", where we dive deep into Linear Regression Analysis with R Software. This isn't a basic tutorial. We are tackling the complexities of real-world datasets and the rigorous diagnostics required for high-stakes decision-making. 𝐈𝐧 𝐭𝐡𝐢𝐬 𝐬𝐞𝐬𝐬𝐢𝐨𝐧, 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐦𝐚𝐬𝐭𝐞𝐫: 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐑𝐨𝐛𝐮𝐬𝐭 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬: Design workflows in R from preprocessing to validation. 𝐃𝐞𝐞𝐩 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐚𝐭𝐢𝐨𝐧: Justify coefficients, effect sizes, and inferential statistics with confidence. 𝐌𝐨𝐝𝐞𝐥 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Use AIC/BIC and cross-validation to select the perfect model. 𝐂𝐨𝐦𝐩𝐥𝐞𝐱 𝐃𝐢𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜𝐬: Learn to identify and resolve multicollinearity and heteroscedasticity. Don’t just predict—prove. 🚀 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐧𝐨𝐰 𝐭𝐨 𝐬𝐞𝐜𝐮𝐫𝐞 𝐲𝐨𝐮𝐫 𝐬𝐩𝐨𝐭: https://lnkd.in/gXGzGAWb #DataScience #RStats #MachineLearning #LinearRegression #BigData #Statistics #EUGlobal #DataAnalytics #CareerGrowth

I’ve just published a Substack post looking at how we analyse compositional proportion data (where outcomes sum to 1), and why Dirichlet regression is often a better choice than modelling each proportion separately. The post walks through: - why treating proportions as independent outcomes can be misleading - how Dirichlet models respect the trade-offs inherent in compositional data - what you gain analytically when interest allocation, time use, or attention is split across categories I re-examine an existing paper by Liam Satchell as a concrete example, showing how a Dirichlet approach could have offered additional insight into relative changes across components (i.e. because the data structure naturally calls for a joint model). If you work with proportions, shares, or allocations (eye-tracking, time budgets, behavioural coding, survey percentages), this approach is often worth considering. P. S. There is also a little tutorial on Ordered Beta regression using the #ordbetareg package in R Link to the post in the comments 👇 #Dirichlet #brms #proportions #composite #mixedeffects #rstats #eyetracking #substack

🚀 Day 8 – Data Manipulation in R Just Got Easier If you can’t manipulate data… You can’t analyze it. Today we unlock one of the most powerful tools in R: dplyr 🔥 This is where beginners become real analysts. With just a few functions, you can: ✔ select() → Choose important columns ✔ filter() → Focus on specific rows ✔ arrange() → Sort data smartly ✔ mutate() → Create new calculated columns No complex code. No messy scripts. Just clean, readable, professional data transformation. 💡 Real Truth: In real-world analytics, 70% of the time is spent cleaning and manipulating data — not building fancy charts. If you master dplyr, you increase your speed, clarity, and confidence. This is not just R programming. This is analytical thinking. 👨🏫 Mentor: Yogesh Sharma 🏢 Powered by Ripocybertech 📌 Day 8 of 10 Days of R Programming Consistency is building future experts. Comment “DPLYR” if you’re serious about becoming a Data Analyst. Save this post for revision. #RProgramming #DataAnalytics #DataScience #dplyr #LearnR #AnalyticsJourney #YogeshSharma

While tinkering with my own kernel, I ran into a detail that made me stop and think about how data actually lives in memory. That moment led me to start writing “Sidecar” articles — short detours into topics I discover along the journey. This first one explores the .data and .bss sections and why zero-initialized data doesn’t even occupy space in your binary. 👇 https://lnkd.in/diVvpqfM

I’ve just published a new article on Medium explaining how to reverse a singly linked list in-place using pointer manipulation. In this story, I walk through the step-by-step execution of the algorithm, breaking down each iteration of the while loop and illustrating how the prev, curr, and next pointers evolve during the reversal process. The focus is on: • Understanding in-place reversal (O(1) space complexity) • Visualizing pointer transitions clearly • Strengthening core data structure fundamentals If you're preparing for coding interviews or revisiting foundational concepts in data structures, this might be a helpful read. I’d appreciate your feedback and thoughts!

Today’s forecasting work was powered by a few core R tools that made time-based analysis a lot more intuitive: • RStudio for scripting and workflow organization • ts() for structuring quarterly and monthly time series data • lubridate to properly parse real-world date formats (because messy dates will humble you quickly 😭) • par(mfrow=) to compare multiple time series behaviors in a single view • Base plotting in R to visualize trend, seasonality, and noise before modeling I’m learning that before any model gets built, the real work starts with preparing, formatting, and actually understanding how your data behaves over time. Forecasting is just the last step while preparation is where the story begins. #BecomingADataScientist #DataScience #BusinessIntelligence #RStats #Forecasting #Analytics #GradSchool